






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class        a       (					lowercase__				,			lowercase__  ):



      """simple docstring"""


      a    :					Dict							=      1
      @register_to_config
      def __init__( self   :     int						, __lowercase   :     int = 1000						, __lowercase   :     Optional[Union[np.ndarray, List[float]]] = None   )				-> Union[str, Any]:
        # set `betas`, `alphas`, `timesteps`
        self.set_timesteps(__lowercase   )

        # standard deviation of the initial noise distribution
        __UpperCAmelCase				:      List[Any]	 =    1.0

        # For now we only support F-PNDM, i.e. the runge-kutta method
        # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
        # mainly at formula (9), (12), (13) and the Algorithm 2.
        __UpperCAmelCase				:      List[Any]	 =    4

        # running values
        __UpperCAmelCase				:      str	 =    []
      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     int						, __lowercase   :     Union[str, torch.device] = None   )				-> int:
        __UpperCAmelCase				:      int	 =    num_inference_steps
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.linspace(1						, 0						, num_inference_steps + 1   )[:-1]
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.cat([steps, torch.tensor([0.0]   )]   )

        if self.config.trained_betas is not None:
          __UpperCAmelCase				:      Dict	 =    torch.tensor(self.config.trained_betas						, dtype=torch.floataa   )
        else:
          __UpperCAmelCase				:      Dict	 =    torch.sin(steps * math.pi / 2   ) ** 2

        __UpperCAmelCase				:      List[Any]	 =    (1.0 - self.betas**2) ** 0.5

        __UpperCAmelCase				:      Tuple	 =    (torch.atana(self.betas						, self.alphas   ) / math.pi * 2)[:-1]
        __UpperCAmelCase				:      Dict	 =    timesteps.to(__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    []
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     torch.FloatTensor						, __lowercase   :     int						, __lowercase   :     torch.FloatTensor						, __lowercase   :     bool = True						, )				-> Union[SchedulerOutput, Tuple]:
        if self.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""   )

        __UpperCAmelCase				:      List[str]	 =    (self.timesteps == timestep).nonzero().item()
        __UpperCAmelCase				:      Optional[Any]	 =    timestep_index + 1

        __UpperCAmelCase				:      List[str]	 =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
        self.ets.append(__lowercase   )

        if len(self.ets   ) == 1:
          __UpperCAmelCase				:      Tuple	 =    self.ets[-1]
        elif len(self.ets   ) == 2:
          __UpperCAmelCase				:      Union[str, Any]	 =    (3 * self.ets[-1] - self.ets[-2]) / 2
        elif len(self.ets   ) == 3:
          __UpperCAmelCase				:      Union[str, Any]	 =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
        else:
          __UpperCAmelCase				:      List[Any]	 =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

        __UpperCAmelCase				:      Union[str, Any]	 =    self._get_prev_sample(__lowercase						, __lowercase						, __lowercase						, __lowercase   )

        if not return_dict:
          return (prev_sample,)

        return SchedulerOutput(prev_sample=__lowercase   )
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     torch.FloatTensor						, *__lowercase   :     Optional[Any]						, **__lowercase   :     Any   )				-> torch.FloatTensor:
        return sample
      def       UpperCAmelCase ( self   :     Tuple						, __lowercase   :     Tuple						, __lowercase   :     List[str]						, __lowercase   :     Optional[int]						, __lowercase   :     Dict   )				-> str:
        __UpperCAmelCase				:      int	 =    self.alphas[timestep_index]
        __UpperCAmelCase				:      Tuple	 =    self.betas[timestep_index]

        __UpperCAmelCase				:      Any	 =    self.alphas[prev_timestep_index]
        __UpperCAmelCase				:      List[str]	 =    self.betas[prev_timestep_index]

        __UpperCAmelCase				:      List[str]	 =    (sample - sigma * ets) / max(__lowercase						, 1e-8   )
        __UpperCAmelCase				:      List[Any]	 =    next_alpha * pred + ets * next_sigma

        return prev_sample




      def __len__( self   :     Tuple   )				-> str:
        return self.config.num_train_timesteps









"""simple docstring"""





from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






